Competitive Normalized Least-Squares Regression
Authors: Jamil, W. and Bouchachia, A.
Journal: IEEE Transactions on Neural Networks and Learning Systems
Volume: 32
Issue: 7
Pages: 3262-3267
eISSN: 2162-2388
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2020.3009777
Abstract:Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and its relevance to a broad range of streaming applications. In this brief, we focus on online regularized regression. We propose a novel efficient online regression algorithm, called online normalized least-squares (ONLS). We perform theoretical analysis by comparing the total loss of ONLS against the normalized gradient descent (NGD) algorithm and the best off-line LS predictor. We show, in particular, that ONLS allows for a better bias-variance tradeoff than those state-of-the-art gradient descent-based LS algorithms as well as a better control on the level of shrinkage of the features toward the null. Finally, we conduct an empirical study to illustrate the great performance of ONLS against some state-of-the-art algorithms using real-world data.
https://eprints.bournemouth.ac.uk/34331/
Source: Scopus
Competitive Normalized Least-Squares Regression.
Authors: Jamil, W. and Bouchachia, A.
Journal: IEEE Trans Neural Netw Learn Syst
Volume: 32
Issue: 7
Pages: 3262-3267
eISSN: 2162-2388
DOI: 10.1109/TNNLS.2020.3009777
Abstract:Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and its relevance to a broad range of streaming applications. In this brief, we focus on online regularized regression. We propose a novel efficient online regression algorithm, called online normalized least-squares (ONLS). We perform theoretical analysis by comparing the total loss of ONLS against the normalized gradient descent (NGD) algorithm and the best off-line LS predictor. We show, in particular, that ONLS allows for a better bias-variance tradeoff than those state-of-the-art gradient descent-based LS algorithms as well as a better control on the level of shrinkage of the features toward the null. Finally, we conduct an empirical study to illustrate the great performance of ONLS against some state-of-the-art algorithms using real-world data.
https://eprints.bournemouth.ac.uk/34331/
Source: PubMed
Competitive Normalized Least-Squares Regression
Authors: Jamil, W. and Bouchachia, A.
Journal: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume: 32
Issue: 7
Pages: 3262-3267
eISSN: 2162-2388
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2020.3009777
https://eprints.bournemouth.ac.uk/34331/
Source: Web of Science (Lite)
Competitive Normalised Least Squares Regression
Authors: Waqas, J. and Bouchachia, A.
Journal: IEEE Transactions on Neural Networks and Learning Systems
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1045-9227
Abstract:Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and its relevance to a broad range of streaming applications. In this paper, we focus on online regularised regression. We propose a novel efficient online regression algorithm, called Online Normalised Least Squares (ONLS). We perform theoretical analysis, by comparing the total loss of ONLS against the Normalised Gradient Descent algorithm (NGD) and the best offline LS predictor. We show in particular that ONLS allows for a better bias-variance tradeoff than those state-of-the-art gradient descent-based LS algorithms as well as a better control on the level of shrinkage of the features towards null. Finally, we conduct an empirical study to illustrate the great performance of ONLS against some state-of-the-art algorithms using real-world data.
https://eprints.bournemouth.ac.uk/34331/
Source: Manual
Competitive Normalized Least-Squares Regression.
Authors: Jamil, W. and Bouchachia, A.
Journal: IEEE transactions on neural networks and learning systems
Volume: 32
Issue: 7
Pages: 3262-3267
eISSN: 2162-2388
ISSN: 2162-237X
DOI: 10.1109/tnnls.2020.3009777
Abstract:Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and its relevance to a broad range of streaming applications. In this brief, we focus on online regularized regression. We propose a novel efficient online regression algorithm, called online normalized least-squares (ONLS). We perform theoretical analysis by comparing the total loss of ONLS against the normalized gradient descent (NGD) algorithm and the best off-line LS predictor. We show, in particular, that ONLS allows for a better bias-variance tradeoff than those state-of-the-art gradient descent-based LS algorithms as well as a better control on the level of shrinkage of the features toward the null. Finally, we conduct an empirical study to illustrate the great performance of ONLS against some state-of-the-art algorithms using real-world data.
https://eprints.bournemouth.ac.uk/34331/
Source: Europe PubMed Central
Competitive Normalised Least Squares Regression
Authors: Waqas, J. and Bouchachia, A.
Journal: IEEE Transactions on Neural Networks and Learning Systems
Volume: 32
Issue: 7
Pages: 3262-3267
ISSN: 1045-9227
Abstract:Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and its relevance to a broad range of streaming applications. In this paper, we focus on online regularised regression. We propose a novel efficient online regression algorithm, called Online Normalised Least Squares (ONLS). We perform theoretical analysis, by comparing the total loss of ONLS against the Normalised Gradient Descent algorithm (NGD) and the best offline LS predictor. We show in particular that ONLS allows for a better bias-variance tradeoff than those state-of-the-art gradient descent-based LS algorithms as well as a better control on the level of shrinkage of the features towards null. Finally, we conduct an empirical study to illustrate the great performance of ONLS against some state-of-the-art algorithms using real-world data.
https://eprints.bournemouth.ac.uk/34331/
Source: BURO EPrints